An overview of image processing in biomedicine using U-Net convolutional neural network architecture

Abstract

Image processing in biomedicine is a very broad field, which includes both medical and technical significance. The aim of this paper is to investigate the current trends in the domain of application of U-Net architecture in the period from 2018 to 2023. The PRISMA framework was used for the systematic literature review, and 4 research questions were asked. For the most part, U-Net architectures are used that can process complex high-resolution images in the fastest way in the context of semantic segmentation. Previous work in image processing has focused on overcoming problems such as the complexity of different architectures, image loss, image resolution, and quality, as well as the size of datasets and noise reduction. The most frequently used groups of datasets are BraTS, Data Science Bowl, and ISIC Challenge. The best general Dice score was obtained for the LUNA16, VESSEL12, and Kaggle Lung datasets with 0.98. It is concluded that the application of the U-Net network is growing, with a focus on solving specific challenges in the context of a certain modality and segment of biomedicine.